BIOS 500 (1) Statistical Methods I Lab: Fall. Prerequisites: Concurrent enrollment in BIOS 500. This lab complements the BIOS 500 course by using hands-on demonstrations of statistical concepts and methods taught in lecture. The statistical software, SAS, will be introduced as a programming tools to accomplish many of these tasks. Sample Syllabus - Labs

BIOS 502 (2) Statistical Methods III: Fall. Prerequisites: BIOS 500 and BIOS 501. This course introduces students to data analytic methods not covered in BIOS 500 and BIOS 501. It is focused on multilevel models, particularly modeling longitudinal data. Other hierarchical models will also be introduced to analyze other types of clustered data. Students will learn how to specify an appropriate statistical model so that specific research questions of interest can be addressed in a methodologically sound way. Sample Syllabus

BIOS 505 (4) Statistics for Experimental Biology: Spring. Intended for PhD candidates in the biological and biomedical sciences. Introduces the most frequently used statistical methods in those fields, including linear regression, ANOVA, logistic regression, and nonparametric methods. Students learn the statistical skills necessary to read scientific articles in their fields, do simple analyses on their own, and be good consumers of expert statistical advice.

BIOS 507 (4) Applied Regression Analysis: Spring. Prerequisites: Biostatistics major, BIOS 506 or equivalent; one year of calculus, linear algebra, and matrix algebra. Both theoretical and applied aspects of linear regression and generalized linear regression modeling will be covered in this course. The emphasis will be on applications. The first part of the course covers the following topics: simple linear regression, multiple linear regression, analysis of variance, confounding and interaction, residual and influence diagnostics, variable transformations, multi-collinearity, model selection and validation. The second part of the course includes: generalized linear models, introduction to maximum likelihood estimation, logistic regression, nominal and ordinal logistic regression, Poisson regression. Parameter interpretation and scientific interpretation of results will be emphasized throughout the course. Sample Syllabus

BIOS 508 (4) Biostatistical Methods: The prerequisites include College-level courses in Linear algebra and Calculus and programming experience in either SAS or R (or concurrent enrollment in BIOS 531: SAS Programming.) This course provides a mathematically sophisticated introduction to the concepts and methods of biostatistical data analysis. It aims to provide the students the skills to collaborate with investigators and statistical colleagues in the analysis of data from biomedical and public health studies and to communicate the results of statistical analyses to a broad audience. The topics include descriptive statistics; probability; detailed development of the binomial, Poisson and normal distributions and simulation of random variables from these distributions; sampling distributions; point and confidence interval estimation; simulation studies; hypothesis testing; power analysis and sample size calculations; a variety of one- and two-sample parametric and non-parametric methods for analyzing continuous or discrete data and resampling statistics. The course will also equip students with computer skills for implementing these statistical methods using standard statistical software SAS or R.

BIOS 509 (4) Applied Linear Models: The course covers statistical methodology for the analysis of continuous outcome data, primarily from cross-sectional studies and designed experiments. We introduce the key matrix-based methods for estimation and inference based on the multiple linear regression model. Subsequently, topics include secondary hypothesis testing and restrictions, regression diagnostics, model selection, confounding and interaction, analysis of variance and covariance, and an introduction to random effects and the mixed linear model.

BIOS 513 (4) Statistical Inference I: Spring, Pre-requisite BIOS 512. Introduces the theory of parameter estimation, interval estimation, and tests of hypotheses. In this course, we emphasize the classical "frequentist" (i.e., Neyman-Pearson-Wald) approach to inference. As time permits, we briefly explore alternative paradigms of inference such as neo-Fisherian, Bayesian, and statistical decision theory. This course is required for Biostatistics MSPH students and typically is taken in the second semester of the first year.

BIOS 516 (1) Introduction to Large- Scale Biomedical Data Analysis: Fall. Prerequisite: BIOS 501 or equivalent, or permission from the instructor. This is the overview course for the Bioinformatics, Imaging and Genetics (BIG) concentration in the PhD program of the Department of Biostatistics and Bioinformatics. It aims to introduce students to modern high-dimensional biomedical data, including data in bioinformatics and computational biology, biomedical imaging, and statistical genetics. This course will be co-taught by all BIG core faculty members, with each faculty member giving one or two lectures. The focus of the course will be on the data characteristics, opportunities and challenges for statisticians, as well as current developments and hot areas of the research fields of bioinformatics, biomedical imaging and statistical genetics.

BIOS 540 (2) Introduction to Bioinformatics: Spring. Prerequisites: BIOS 500, 501, 506, or permission of instructor. This course is an introduction to the field of Bioinformatics for students with a quantitative background. The course covers biological sequence analysis, introductions to genomics, transcriptomics, proteomics and metabolomics, as well as some basic data analysis methods associated with the high-throughput data. In addition, the course introduces concepts such as curse of dimensionality, multiple testing and false discovery rate, and basic concepts of networks. Sample Syllabus

BIOS 544 (2) Introduction to R programming for Non-BIOS students: Fall. Prerequisites: BIOS 500 and BIOS 501. The goal of the course is to will provide an introduction to R in organizing, analyzing, and visualizing data. Once you've completed this course you'll be able to enter, save, retrieve, summarize, display and analyze data.

BIOS 555 (2) High-Throughput Data Analysis using R and BioConductor: Fall. Prerequisites: BIOS 501 or equivalents. Basic programming experience in R. This course covers the basics of microarray and second-generation sequencing data analysis using R/Bio Conductor and other open source software. Topics include gene expression microarray, RNA-seq, ChIP-seq and general DNA sequence analyses. We will introduce technologies, data characteristics, statistical challenges, existing methods and potential research topics. Students will also learn to use proper Bioconductor packages and other open source software to analyze different types of data and deliver biologically interpretable results.

BIOS 560R (VC) Current Topics in Biostatistics: Fall and spring. A faculty member offers a new course on a current topic of interest for both PhD and master’s students. Sample Syllabus

BIOS 570 (2) Introduction to Statistical Genetics: Spring. No prerequisites. This is an introductory course for graduate students in Biostatistics, Bioinformatics, Epidemiology, Genetics, Computational Biology, and other related quantitative disciplines. The course will cover statistical methods for the analysis of family and population based genetic data. Topics covered will include classical linkage analysis, population-based and family‐based association analysis, haplotype analysis, genome‐wide association studies, basic principles in population genetics, imputation-based analysis, pathway‐based analysis, admixture mapping, analysis of copy number variations, and analysis of massively parallel sequencing data. Students will be exposed to the latest statistical methodology and computational tools on gene mapping for complex human diseases.

BIOS 580 (2) Statistical Practice I: Fall. This course will cover topics dedicated to preparing students to collaborate as biostatisticians for public health and biomedical projects with non-statisticians. Covered topics will include consulting versus collaboration, ethics, non-statistical aspects of collaboration (e.g. interpersonal communication), and negotiating expectations with clients. The students will work together in small groups to develop research questions based on an existing real-life datasets and discussions with clinical collaborators, conduct power analyses, choose the appropriate statistical methodology to analyze the research questions, then answer at least one of the questions and present the results in both oral and written format. In addition, individually each student will complete a series of milestones that results in an oral and/or written proposal for an individual capstone project to be completed in the Spring semester.

BIOS 581 (2) Statistical Practice II (Capstone): Spring. This is a required course for the MPH and MSPH students in the Biostatistics and Bioinformatics program in their final spring semester. The purpose of the course is to help students with their capstone project in project management, manuscript writing, and oral presentation while they conduct their project with their individual BIOS advisors. Students will review journal articles to critique study design and statistical analysis methods in a journal club format. They will learn how to write journal articles section by section through lectures and homework assignments. They will develop a manuscript based on their capstone project. At the end of the semester, each student will give an oral presentation on his/her capstone project. Each student will also make a poster on his/her capstone project. Students will receive feedbacks from their peers and instructors to improve their writing and presentation skills. The prerequisite is BIOS 580 - Statistical Practice I.

BIOS 590R (1) Seminar in Biostatistics: Fall and spring. Features invited speakers, departmental faculty, students, and others who discuss special topics and new research findings. (Satisfactory/unsatisfactory grading only.)

BIOS 595 Applied Practice Experience: An Applied Practice Experience (APE) is a unique opportunity that enables students to apply practical skills and knowledge learned through coursework to a professional public health setting that complements the student’s interests and career goals. The APE must be supervised by a Field Supervisor and requires approval from an APE Advisor designated by the student’s academic department at Rollins. Registration for the course is required.

BIOS 598R (VC) Special Projects: Involves intern-like participation on specific scholarly, research, or developmental projects that expose students to the role of the statistical consultant or collaborator in a variety of research settings.

BIOS 709 (4) Generalized Linear Models: Spring. Prerequisites: BIOS 511 and BIOS 707. Studies analysis of data, using generalized linear models as well as models with generalized variance structure. Parametric models include exponential families such as normal, binomial, Poisson, and gamma. Iterative reweighted least squares and quasi-likelihood methods are used for estimation of parameters. Studies methods for examining model assumptions. Introduces generalized estimating equations (GEE) and quadratic estimating equations for problems where no distributional assumptions are made about the errors except for the structure of the first two moments. Illustrations with data from various basic science, medicine, and public health settings. Sample Syllabus

BIOS 731 (2) Advanced Statistical Computing: Fall.* Prerequisites: BIOS 510, 511 and prior programming experience, or permission from one of the instructors. This course covers the theories and applications of some common statistical computing methods. Topics include Markov chain Monte Carlo (MCMC), hidden Markov model (HMM), Expectation-Maximization (EM) and Minorization-Maximization (MM), and optimization algorithms such as linear and quadratic programming. The class has two main goals for students: (1) learn the general theory and algorithmic procedures of some widely used statistical models; (2) develop fluency in statistical programming skills. The class puts more emphasis on implementation instead of statistical theories. Students will gain computational skills and practical experiences on simulations and statistical modeling.

BIOS 732 (2) Advanced Numerical Methods: Fall.* Prerequisites include BIOS 532, BIOS 710 and BIOS 711, or permission of the instructor. BIOS 711 may be taken concurrently. The course covers topics in traditional numerical analysis specifically relevant to statistical estimation and inference. The topics covered include numerical linear algebra, the root finding problem (maximum likelihood) methods such as IRLS, Newton-Raphson, and EM algorithm, and Bayesian techniques for marginalization and sampling for use in statistical inference (MCMC methods). Additional topics may include numerical integration and curve fitting. Sample Syllabus

BIOS 737 (2) Spatial Analysis of Public Health Data: Spring.* Prerequisites: BIOS 506, 507, 510, 511. Familiarizes students with statistical methods and underlying theory for the spatial analysis of georeferenced public health data. Topics covered include kriging and spatial point processes. Includes a review of recent computational advances for applying these methods.

BIOS 738 (2) Bayesian and Empirical Bayes Methods: Fall.* Prerequisites: BIOS 510 and BIOS 511. Includes Bayesian approaches to statistical inference, point and interval estimation using Bayesian and empirical Bayesian methods, representation of beliefs, estimation of the prior distribution, robustness to choice of priors, conjugate analysis, reference analysis, comparison with alternative methods of inference, computational approaches, including Laplace approximation, iterative quadrature, importance sampling, and Markov Chain Monte Carlo (Gibbs sampling). Various applications, such as small area estimation, clinical trials, and other biomedical applications, will be used.

BIOS 745R (1) Biostatistical Consulting: Fall. Prerequisites: BIOS 507 or 509. This course will cover topics dedicated to preparing doctoral students to lead biostatistical collaborations with non-statisticians in public health, biology, and medicine academic environments. Covered collaboration topics will include consulting versus collaboration, ethics, non-statistical aspects of collaboration (e.g. interpersonal communication), and negotiating expectations with clients. Covered biostatistical topics will include specific aim refinement, appropriate study design for the research question, assessment of feasibility (time and effort) of different statistical methods for the same problem, statistical review of grant proposals including power calculations, and appropriate summarization/presentation of results to non-statistical audiences. Sample Syllabus

BIOS 760R (VC) Current Topics in Biostatistics: Fall and spring. A faculty member offers a new course on an advanced topic of interest, such as spatial analysis, time series, missing data methods, causal inference, and discrete multivariate analysis.

BIOS 777 (1) How to Teach Biostatistics: Fall. Prerequisites: BIOS 507, BIOS 511, and summer TATTO workshop. Prepares students for teaching introductory level courses in biostatistics. The topics discussed are: syllabus development, lecturing, encouraging and managing class discussion, evaluating student performance, test and examinations, cheating, the role of the teaching assistant, teacher-student relationships, teaching students with weak quantitative skills, teaching students with diverse backgrounds, teaching health sciences students, teaching medical students, use of audio-visual techniques, and use of computers. Each student is required to teach a certain subject to the other students and the instructor, followed by a discussion of presentation strengths and weaknesses. Sample Syllabus

BIOS 780R (1) Research Methods in Biostatistics: Spring. Prerequisite: BIOS 511. Acquaints students with a variety of areas of biostatistical research and provides the chance to do preliminary reading in an area of interest. Each student reads a few papers in an area of interest and presents the material to the group. Topics and readings can be suggested by the faculty member in charge or by the students. This course may be repeated for credit. (Satisfactory/unsatisfactory grading only.)

BIOS 790R (1) Advanced Seminar in Biostatistics: Fall and spring. Invited speakers, faculty, and advanced students discuss special topics and new research findings. (Satisfactory/unsatisfactory grading only.)